初始化项目，由ModelHub XC社区提供模型

Model: Fordentinc/book-builder-bookwriter-v1
Source: Original Platform

inference_example.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+"""
+Minimum-viable inference example for Fordentinc/book-builder-bookwriter-v1.
+
+Loads Qwen 2.5 7B base + the LoRA adapter, reads a bible file from disk,
+generates a chapter, writes it to stdout.
+
+Usage:
+    pip install torch transformers peft accelerate
+    python inference_example.py path/to/your_bible.txt
+"""
+import sys, torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+BASE = "Qwen/Qwen2.5-7B"
+ADAPTER = "Fordentinc/book-builder-bookwriter-v1"
+
+def main():
+    if len(sys.argv) < 2:
+        print("usage: python inference_example.py <bible.txt>", file=sys.stderr)
+        sys.exit(1)
+    prompt = open(sys.argv[1]).read()
+    if "### Chapter" not in prompt:
+        print("ERROR: bible must end with '### Chapter\\n<title>\\n\\n'", file=sys.stderr)
+        sys.exit(2)
+
+    print(f"Loading base: {BASE}", file=sys.stderr)
+    tok = AutoTokenizer.from_pretrained(ADAPTER)
+    base = AutoModelForCausalLM.from_pretrained(
+        BASE, torch_dtype=torch.bfloat16, device_map="auto",
+    )
+    print(f"Loading adapter: {ADAPTER}", file=sys.stderr)
+    model = PeftModel.from_pretrained(base, ADAPTER)
+    model.eval()
+
+    print(f"Prompt length: {len(prompt)} chars", file=sys.stderr)
+    inputs = tok(prompt, return_tensors="pt").to(model.device)
+
+    with torch.inference_mode():
+        out = model.generate(
+            **inputs,
+            max_new_tokens=2048,
+            do_sample=True,
+            temperature=0.8,
+            top_p=0.95,
+            repetition_penalty=1.05,
+            pad_token_id=tok.eos_token_id,
+        )
+
+    full = tok.decode(out[0], skip_special_tokens=True)
+    # Print only the generated continuation (strip the bible)
+    print(full[len(prompt):])
+
+if __name__ == "__main__":
+    main()